| 1 |
Author(s):
Dr. Sridhar Reddy.
Page No :
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AI-Driven Early Detection of Rare Genetic Disorders in Neonates
Abstract
Early detection of rare genetic disorders in neonates is crucial for timely intervention and improved clinical outcomes. Traditional diagnostic methods face limitations including lengthy turnaround times, fragmented data, and reliance on clinician expertise, which often delay diagnosis. Artificial Intelligence (AI), through advanced machine learning and deep learning algorithms, offers a transformative approach by rapidly analyzing complex genomic, phenotypic, and clinical data to identify patterns indicative of rare diseases. Integrating AI with neonatal screening programs enhances diagnostic accuracy, reduces the diagnostic odyssey, and enables personalized care. Despite challenges such as data privacy, algorithmic bias, and ethical considerations, ongoing advancements in AI and collaborative efforts promise to revolutionize neonatal care. This article explores the role of AI in genomic and phenotypic data analysis, real-world applications, benefits, challenges, and future prospects of AI-driven early detection of rare genetic disorders in neonates.
| 2 |
Author(s):
Mamatha Gugulothu.
Page No : 1-4
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Securing Data Endpoints in Google Cloud: A Data-Driven Approach to Anomaly Detection and Threat Mitigation
Abstract
Using Google Cloud requires a complete defensive plan combining data-based anomaly alerts with early threat response systems to protect confidential information. Modern cloud environments require active security monitoring because their complexity continues to increase while cyber threats become more complex. Traditional security practices fail to handle the fluid characteristics of cloud-based data breaches because organizations must move towards security systems based on data intelligence. The research presents an analysis of advanced anomaly detection methods through statistical analysis, machine learning algorithms, and behavior analytics for observing and responding to abnormal data access patterns and security incidents in Google Cloud infrastructures. Organizations that deploy robust anomaly detection systems create better capabilities for threat detection, along with threat mitigation, and ensure compliance with strict regulatory requirements. Companies require integrated security platforms because cyber dangers combine with financial problems and system breakdowns within a single detection foundation.
| 3 |
Author(s):
Dev Kukreja.
Page No : 1-4
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A Study on Welfare of Employees for Job Satisfaction
Abstract
Employee weal plays a vital part in promoting job satisfaction and enhancing organizational productivity. This exploration explores the relationship between hand weal schemes and job satisfaction within an organizational environment. The study aims to identify crucial weal measures and assess their effectiveness through primary data collection and analysis
| 4 |
Author(s):
Dr Varun Bal.
Page No : 1-6
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Artificial Intelligence in the 2020s: Key Trends, Innovations, and Future Directions
Abstract
Artificial Intelligence (AI) has emerged as a transformative force across multiple sectors, redefining business operations, social interactions, and policy frameworks. This study explores recent trends in AI development and deployment, with a specific focus on advancements between 2020 and 2025. Drawing on academic literature, industry reports, and global policy developments, the paper highlights the rise of generative AI, increasing investments, sector-specific applications, and growing concerns around ethics and governance. Using a systematic literature review methodology, it identifies six dominant themes: the proliferation of generative models, ethical and regulatory challenges, labor market impacts, geopolitical dynamics, and sectoral integration. The paper concludes that while AI presents substantial opportunities for innovation and economic growth, it also necessitates robust policy mechanisms and responsible integration strategies to address emerging risks. The findings provide valuable insights for researchers, practitioners, and policymakers navigating the complex AI landscape in India and globally.
| 5 |
Author(s):
YASH PATEL.
Page No : 1-6
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A Study on Customer Satisfaction Towards ZUDIO, Pandri Raipur c.g.
Abstract
Customer satisfaction is a crucial metric for business success, especially in retail fashion. This study evaluates customer satisfaction at ZUDIO, Pandri Raipur, focusing on product variety, quality, physical evidence, and overall store experience. Using primary data from 50 customers, the research identifies satisfaction levels and areas for improvement.
| 6 |
Author(s):
Dr. Padma Latha .
Page No : 1-7
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AI-Driven Analysis of Patient Feedback for Quality Improvement in Healthcare Services
Abstract
Artificial Intelligence (AI) has emerged as a powerful tool for analyzing patient feedback to drive quality improvement in healthcare services. By leveraging advanced natural language processing and machine learning techniques, AI enables the efficient processing of large volumes of unstructured patient comments from diverse sources such as surveys, social media, and online reviews. This facilitates the extraction of meaningful insights related to patient satisfaction, common concerns, and areas needing improvement. AI-driven feedback analysis supports healthcare providers in making data-informed decisions, enhancing patient-centered care, and optimizing operational workflows. Despite challenges including data privacy, algorithmic bias, and integration into clinical settings, ongoing innovations in AI offer promising avenues for more transparent, equitable, and proactive healthcare quality management. This article reviews AI methodologies, practical applications, ethical considerations, and future trends in transforming patient feedback into actionable intelligence that can significantly improve healthcare delivery.
| 7 |
Author(s):
Supritha Bhandarkar.
Page No : 1-7
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AI-Driven Tools for Assessing and Managing Chronic Pain
Abstract
Chronic pain affects millions globally, presenting significant challenges due to its complex, multifactorial nature and reliance on subjective assessments. AI-driven tools offer a transformative approach by enabling objective, continuous, and personalized pain evaluation and management. Leveraging machine learning, natural language processing, wearable biosensors, and multimodal data integration, these technologies enhance pain assessment accuracy, facilitate early detection of exacerbations, and support tailored treatment plans. Despite promising benefits such as improved patient outcomes, reduced opioid dependency, and enhanced healthcare efficiency, challenges including data privacy, ethical considerations, algorithmic bias, and clinician-patient acceptance must be addressed. This article explores the current landscape of AI applications in chronic pain care, data sources and collection methods, implementation barriers, and ethical implications. It further discusses future research directions, emphasizing the potential of AI to shift chronic pain management from reactive symptom control to proactive, holistic, and patient-centered care.
| 8 |
Author(s):
Ashwin Naik.
Page No : 1-7
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Developing AI Algorithms for Personalized Cancer Immunotherapy Plans
Abstract
Cancer immunotherapy has transformed oncology by enabling the immune system to target tumors, yet significant variability in patient responses underscores the urgent need for personalized treatment approaches. Artificial Intelligence (AI), through advanced machine learning and deep learning techniques, offers powerful tools to analyze complex, multi-dimensional patient data—including genomics, proteomics, imaging, and clinical records—to predict individual responses, optimize therapy selection, and monitor treatment outcomes dynamically. This article explores the development of AI algorithms tailored for personalized cancer immunotherapy planning, detailing the data integration processes, modeling strategies, and personalization frameworks that enable precise and adaptive treatment regimens. It also addresses challenges such as data heterogeneity, model interpretability, ethical considerations, and regulatory hurdles. By reviewing current clinical applications and envisioning future innovations, the article highlights AI’s transformative potential to enhance diagnostic accuracy, improve patient outcomes, and reduce adverse effects in cancer immunotherapy. Multidisciplinary collaboration and patient-centered design are essential to realize AI-driven precision oncology, ultimately advancing more effective and safer cancer care worldwide.
| 9 |
Author(s):
Premi S.
Page No : 1-8
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Talent Acquisition process with reference to Toppaz Industries
Abstract
This project report, titled “Talent Acquisition Process with Reference to Topaaz Industries,” examines the company’s strategic approach to acquiring top talent. Unlike traditional recruitment, talent acquisition is an ongoing, data-driven process involving workforce planning, employer branding, sourcing, assessment, and onboarding. The study highlights that while the process is generally effective, external market competition and internal practices impact its success. Statistical analyses show the importance of continuous improvement, inclusive hiring, and a candidate-focused strategy to enhance recruitment outcomes and support long-term organizational growth.
| 10 |
Author(s):
Dr. Manjunath Gowda.
Page No : 1-8
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AI-Enhanced Monitoring Systems for Managing Chronic Respiratory Diseases
Abstract
Chronic respiratory diseases (CRDs) such as asthma and chronic obstructive pulmonary disease (COPD) pose substantial health challenges globally, requiring continuous monitoring for effective management. Traditional monitoring methods are often limited by episodic data collection and subjective assessments, which can delay intervention and worsen patient outcomes. Artificial intelligence (AI) offers transformative potential to overcome these limitations by integrating data from wearable sensors, environmental monitors, and electronic health records to enable real-time, personalized monitoring. AI algorithms can detect early signs of disease exacerbation, predict risk, and support tailored interventions, shifting care from reactive to proactive models. This article reviews current AI applications in CRD monitoring, explores the components of AI-enhanced systems, discusses clinical benefits, and addresses challenges such as data privacy, model bias, and integration barriers. We also highlight future innovations poised to advance precision respiratory medicine, improving patient quality of life while reducing healthcare costs. Through multidisciplinary collaboration and ethical implementation, AI-driven monitoring systems promise to revolutionize chronic respiratory disease management worldwide.
| 11 |
Author(s):
Dr. Shankar C. Patil.
Page No : 1-8
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AI-Enhanced Screening Tools for Early Detection of Neurodegenerative Diseases
Abstract
Neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and ALS pose significant global health challenges due to their progressive nature and often late diagnosis. Early detection is crucial for effective intervention and improved patient outcomes, yet current screening methods face limitations in sensitivity, accessibility, and objectivity. Artificial intelligence (AI) offers transformative potential by analyzing complex multimodal data—including neuroimaging, genetic profiles, clinical records, and behavioral metrics—to identify subtle early signs of neurodegeneration. This article explores the fundamentals of AI in medical screening, advances in AI-enhanced imaging analysis, integration of multimodal datasets, and AI-powered cognitive and behavioral assessments. Real-world clinical applications and case studies illustrate the benefits and current challenges of AI deployment, including ethical, legal, and privacy considerations. Future innovations, such as federated learning, wearable biosensors, and explainable AI, promise to further enhance early detection and personalized screening. Responsible development and multidisciplinary collaboration are essential to maximize AI’s impact in proactive neurodegenerative disease management.
| 12 |
Author(s):
Selva Raj.
Page No : 1-8
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Developing AI Models for Predicting Hospital Readmission Rates
Abstract
Artificial Intelligence (AI) has emerged as a transformative tool for predicting hospital readmission rates, a critical challenge in healthcare that impacts patient outcomes and costs. By leveraging machine learning algorithms and integrating diverse data sources—such as electronic health records, patient demographics, clinical histories, and social determinants—AI models can accurately identify patients at high risk for readmission. These models enable healthcare providers to implement targeted interventions, improving care quality and reducing avoidable hospitalizations. Despite challenges including data quality issues, model interpretability, integration barriers, and ethical considerations, ongoing advancements in AI techniques and collaborative efforts among clinicians, data scientists, and policymakers hold promise for more effective, equitable, and scalable predictive solutions. This article reviews the fundamentals of AI in healthcare, data preparation strategies, model development processes, real-world applications, and future directions for enhancing hospital readmission prediction, emphasizing the critical role of AI in transforming patient care and healthcare management.
| 13 |
Author(s):
Nancy Rohilla.
Page No : 1-11
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HISTORY AND EVOLUTION OF POLICE SYSTEM IN INDIA
Abstract
The evolution of the police system in India traces a profound transformation from informal communitybased models of security in ancient times to a centralized bureaucratic structure under British rule and the continued legacy of that model in independent India. Ancient Indian scriptures and epics like Manusmriti and Arthashastra reveal that structured policing and crime categorization existed as early as the Vedic and Mauryan periods. Medieval India saw a fusion of military and administrative functions in policing, especially during the Mughal and Maratha regimes. However, modern institutionalization began during the British era with the Police Act of 1861, which created a centralized, hierarchical, and often repressive force. Although several commissions and reforms have followed since independence, the colonial ethos and structure largely remain intact. This historical overview underscores the need for comprehensive police reform aligned with democratic ideals and public accountability in India.
| 14 |
Author(s):
Amrita Rastogi.
Page No : 1-11
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WIRELESS SECURITY IN IoT : A NOVEL APPROACH FOR PREVENTING MAN-IN-THE MIDDLE ATTACKS
Abstract
The Internet of Things (IoT) has rapidly transformed several industries, including transportation, smart homes, healthcare, and industrial automation. However, with the increasing reliance on the inter-relatedness of IoT devices, we face significant security threats, such as Man-in-the-Middle (MitM) attacks and Distributed Denial-of-Service (DDoS) attacks. MitM attacks allow attackers to listen to and manipulate communication between the device, leading to data exposure and unauthorized access, while DDoS attacks consume network resources, reducing device life expectancy and increasing energy usage. This research proposes a security framework to mitigate MitM and DDoS attacks in IoT and wireless sensor networks (WSNs). This framework utilizes strong encryption solutions, mutual authentication protocols, and blockchain-based trust management to support security while lowering computational overhead. The proposed framework prevents unauthorized access through lightweight ciphering approaches appropriate for resource-limited IoT devices, while blockchain technology utilizes a decentralized, tamper-proof ledger for device authentication based on communication logs. Proposed research identifies and discusses important security and privacy challenges: linkability, unauthorized communication, and side-channel attacks.
| 15 |
Author(s):
Utkarsh Barman,Sumit Koul(Associate Professor),School of Business.
Page No : 1-11
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ROLE OF AI IN RECRUITMENT & TALENT ACQUISITION
Abstract
Artificial Intelligence (AI) has significantly reshaped the landscape of recruitment and talent acquisition. Traditional hiring methods, which relied heavily on manual efforts and subjective judgments, often faced inefficiencies, bias, and delays. In contrast, AI-powered tools—such as Applicant Tracking Systems (ATS), intelligent chatbots, predictive analytics, and automated video assessments—are revolutionizing how companies identify and engage talent.
This report explores the role of AI in enhancing hiring outcomes by improving speed, reducing human error, and fostering fairer recruitment practices. It also critically examines challenges, including ethical dilemmas, data security concerns, and the risk of algorithmic bias. Real-world examples like Unilever’s success and Amazon’s setbacks with AI-driven recruitment provide insight into the diverse impact of these technologies.
With credible industry data from sources such as LinkedIn, Gartner, and PwC, this study outlines how AI is set to redefine recruitment by drastically shortening hiring cycles and improving candidate experience. However, the report also emphasizes the need for responsible AI use, highlighting the importance of human oversight to maintain fairness, transparency, and integrity in talent acquisition.
| 16 |
Author(s):
Harsh Rai.
Page No : 1-16
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Consumer Behaviour and Decision-Making Patterns in the Used Indian Automobiles Industry in India
Abstract
The used Automobile industry in India has undergone a remarkable transformation over the past
decade, driven by evolving consumer preferences, increasing urbanization, and the rapid integration
of digital technologies. Traditionally dominated by unorganized players—local dealers, individual
sellers, and informal networks—this sector often lacked transparency, standardization, and customer
confidence. However, the emergence of digital platforms such as CARS24, Spinny, and CarDekho
has revolutionized the landscape, offering consumers a more organized, convenient, and trustworthy
way to buy and sell pre-owned vehicles.
Among these, Indian automobile industry has emerged as a frontrunner by leveraging data-driven
solutions and technology-enabled services. It provides features such as instant car valuation, online
listings, vehicle inspection at the doorstep, seamless documentation, and digital transactions. These
innovations have addressed many of the inefficiencies associated with the traditional used car market,
such as restricted market access, unclear pricing, and time-consuming negotiations.
Despite these advancements, consumer behavior in the digital used car segment remains influenced
by a variety of psychological and functional factors. Elements such as trust in digital platforms,
perceived risks (related to payments, authenticity, and quality), and expectations around service and
after-sales support continue to shape consumer decisions. Many first-time users, especially in semi
urban and rural areas, remain cautious due to concerns about fraud, hidden charges, and limited
awareness of digital processes.
This study aims to analyze the consumer decision-making process in India’s used car market, with a
special focus on digital platforms like CARS24, Car Dekho, Spinny, etc. It will examine key
influencing factors such as price sensitivity, trust, risk perception, platform usability, and service
quality. The research is based on secondary data from industry reports, market research studies, and
academic sources to identify the emerging patterns of consumer behavior in this domain. The insights
generated from this study will help digital automotive platforms like CARS24, Spinny, Car Dekho,
etc., enhance customer experience and build stronger engagement strategies.
| 17 |
Author(s):
Dr. Biswajit Satpathy.
Page No : 1-16
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A TISM MODEL WITH THE TRIPLE BOTTOM LINE (TBL) AS THE ULTIMATE OBJECTIVE FROM THE BHAGAVAD GITA
Abstract
This work aspires to combine classical philosophical views with contemporary sustainability frameworks by formulating a Total Interpretive Structural Modelling (TISM) model that associates the eighteen chapters of the Bhagavad Gita with the Triple Bottom Line (TBL) paradigm, which encompasses social, environmental, and economic realms. Utilizing a qualitative-exploratory methodology, each chapter of the Gita is conceptualized as a variable within this analytical framework, thereby elucidating interconnections that collectively contribute to sustainable practices. The analysis employs Interpretive Structural Modelling (ISM) and MICMAC methodologies to delineate the hierarchical relationships, revealing foundational, integrative, and dependent chapters that collectively constitute a progressive ethical evolution. The findings indicate that the philosophical and ethical insights of the Gita—most notably those concerning self-awareness, duty, and detachment—provide a pragmatic, values-oriented framework for sustainable leadership and organizational strategies. By synthesizing spiritual insights with modern sustainability paradigms, this research advocates for a trans-disciplinary approach to enhancing organizational resilience and ethical governance, thereby positioning the Bhagavad Gita as an enduring guide for addressing contemporary challenges.